Semantic annotation of sensor data with overlapping physical features

ABSTRACT

A method for semantic annotation of sensor data may include obtaining sensor data representing an image of a geographic area. The boundary points defining a first polygon in the image of the geographic area may be determined based on the sensor data. An overlap between the first polygon and a second polygon in the image of the geographic area may be detected based at least on the boundary points defining the first polygon. At least one of the first polygon or the second polygon may be modified to remove the overlap between the first polygon and the second polygon. An annotation corresponding to the first polygon may be generated based on the modifying of at least one of the first polygon or the second polygon. The annotation may identify a physical feature within the geographic area. Related systems and computer program products are also provided.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.63/303,268, entitled “SEMANTIC ANNOTATION OF SENSOR DATA WITHOVERLAPPING PHYSICAL FEATURES” and filed on Jan. 26, 2022, thedisclosure of which is incorporated herein by reference in its entirety.

BACKGROUND

An autonomous vehicle may be capable of sensing its surroundingenvironment and navigating with minimal to no human input. In order tosafely traverse a selected path while avoiding obstacles that may bepresent along the way, the vehicle may rely on various types of maps.The maps may include semantic labels that enable the vehicle todistinguish between different physical features present in the vehicle'ssurrounding environment. Nevertheless, annotating a map, such as abirds-eye map or a street-level map, to include accurate and consistentsemantic labels can be a difficult and resource-intensive task.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an example environment in which a vehicle including one ormore components of an autonomous system can be implemented;

FIG. 2 is a diagram of one or more systems of a vehicle including anautonomous system;

FIG. 3 is a diagram of components of one or more devices and/or one ormore systems of FIGS. 1 and 2 ;

FIG. 4A is a diagram of certain components of an autonomous system;

FIG. 4B is a diagram of an implementation of a neural network;

FIGS. 4C and 4D are a diagram illustrating example operation of aconvolutional neural network (CNN);

FIG. 5A is a block diagram illustrating an example of a perceptionsystem for generating semantic annotations for a geographic area;

FIG. 5B is a diagram illustrating an example process of traffic laneextraction from sensor data and/or geographic data;

FIG. 5C is a diagram illustrating an example process of filtering laneedges;

FIG. 5D is a diagram illustrating an example process of filteringoverlapping lane polygons;

FIG. 5E is a diagram illustrating an example process of filtering andlinking lane intersections;

FIG. 5F is an example processed image generated by a physical featureextractor; and

FIG. 6 is a flowchart illustrating an example of a process forannotating sensor data.

DETAILED DESCRIPTION

In the following description numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure forthe purposes of explanation. It will be apparent, however, that theembodiments described by the present disclosure can be practiced withoutthese specific details. In some instances, well-known structures anddevices are illustrated in block diagram form in order to avoidunnecessarily obscuring aspects of the present disclosure.

Specific arrangements or orderings of schematic elements, such as thoserepresenting systems, devices, modules, instruction blocks, dataelements, and/or the like are illustrated in the drawings for ease ofdescription. However, it will be understood by those skilled in the artthat the specific ordering or arrangement of the schematic elements inthe drawings is not meant to imply that a particular order or sequenceof processing, or separation of processes, is required unless explicitlydescribed as such. Further, the inclusion of a schematic element in adrawing is not meant to imply that such element is required in allembodiments or that the features represented by such element may not beincluded in or combined with other elements in some embodiments unlessexplicitly described as such.

Further, where connecting elements such as solid or dashed lines orarrows are used in the drawings to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not illustrated in the drawings so as not toobscure the disclosure. In addition, for ease of illustration, a singleconnecting element can be used to represent multiple connections,relationships or associations between elements. For example, where aconnecting element represents communication of signals, data, orinstructions (e.g., “software instructions”), it should be understood bythose skilled in the art that such element can represent one or multiplesignal paths (e.g., a bus), as may be needed, to affect thecommunication.

Although the terms first, second, third, and/or the like are used todescribe various elements, these elements should not be limited by theseterms. The terms first, second, third, and/or the like are used only todistinguish one element from another. For example, a first contact couldbe termed a second contact and, similarly, a second contact could betermed a first contact without departing from the scope of the describedembodiments. The first contact and the second contact are both contacts,but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is included for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well and can be used interchangeably with “one ormore” or “at least one,” unless the context clearly indicates otherwise.It will also be understood that the term “and/or” as used herein refersto and encompasses any and all possible combinations of one or more ofthe associated listed items. It will be further understood that theterms “includes,” “including,” “comprises,” and/or “comprising,” whenused in this description specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the terms “communication” and “communicate” refer to atleast one of the reception, receipt, transmission, transfer, provision,and/or the like of information (or information represented by, forexample, data, signals, messages, instructions, commands, and/or thelike). For one unit (e.g., a device, a system, a component of a deviceor system, combinations thereof, and/or the like) to be in communicationwith another unit means that the one unit is able to directly orindirectly receive information from and/or send (e.g., transmit)information to the other unit. This may refer to a direct or indirectconnection that is wired and/or wireless in nature. Additionally, twounits may be in communication with each other even though theinformation transmitted may be modified, processed, relayed, and/orrouted between the first and second unit. For example, a first unit maybe in communication with a second unit even though the first unitpassively receives information and does not actively transmitinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and transmits theprocessed information to the second unit. In some embodiments, a messagemay refer to a network packet (e.g., a data packet and/or the like) thatincludes data.

As used herein, the term “if” is, optionally, construed to mean “when”,“upon”, “in response to determining,” “in response to detecting,” and/orthe like, depending on the context. Similarly, the phrase “if it isdetermined” or “if [a stated condition or event] is detected” is,optionally, construed to mean “upon determining,” “in response todetermining,” “upon detecting [the stated condition or event],” “inresponse to detecting [the stated condition or event],” and/or the like,depending on the context. Also, as used herein, the terms “has”, “have”,“having”, or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based at least partially on”unless explicitly stated otherwise.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments can be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

General Overview

In some aspects and/or embodiments, systems, methods, and computerprogram products described herein include and/or implement semanticannotation of sensor data. A vehicle (e.g., an autonomous vehicle) mayutilize sensor data for navigation, localization, and/or the like. Thesensor data may be annotated to include one or more semantic labelsidentifying the physical features that are present in a geographicalarea around the vehicle. For example, the semantic labels may begenerated based on the geographic data and a drivability mask (e.g., atop-view drivable mask) identifying areas (e.g., surfaces) where thevehicle can drive. In some instances, the drivability mask may begenerated based on sensor data, which may include one or more images ofthe geographic area including, for example, a birds-eye view image, astreet-level image, a point cloud image, and/or the like.

Semantic annotation adds, to the sensor data, semantic labelsrepresentative of the meaning and context of that sensor data. Semanticunderstanding, in turn, enables a machine (e.g., the vehicle or one ormore related systems) to process and interpret the sensor data as ahuman might. Accordingly, semantic understanding represents a specifictype of “computer vision”—a field of technology that attempts to enablecomputers to “see” the world in a manner similar to a human being. Asdescribed above, sensor data may represent an image of a geographic areaaround the vehicle (e.g., a birds-eye image, a street-level image, apoint cloud image, and/or the like). Semantic annotations may designatecertain portions of that image as representing one or more physicalfeatures present within the geographic area around the vehicle. Forexample, an area in the image may be assigned a semantic labeldesignating that area as a traffic lane (e.g., a drivable surface formotorized vehicles, for bikes, etc.), a crosswalk, an intersection, atraffic signal, a traffic sign, and/or the like. However, overlapsbetween multiple physical features, such as when two or more trafficlanes merge or intersect, can prevent an accurate and consistentassignment of semantic labels to the physical features present withingeographic data.

By virtue of the implementation of systems, methods, and computerprogram products described herein, techniques for processing sensor datafor annotation may be provided. Semantic understanding of physicalfeatures present within the sensor data may be important for a number oftasks including programmatic navigation of a corresponding geographicarea by a vehicle. As discussed in more detail below, the presentdisclosure relates to an improvement in generating semantic labels forsensor data, which includes techniques for improving differentiation ofdifferent physical features present in the sensor data.

The embodiments disclosed herein improve the ability of computingsystems, such as computing devices included within or supportingoperation of a vehicle (e.g., an autonomous vehicle), to annotate sensordata with semantic labels that enables the vehicle to distinguishbetween different physical features within a geographic area around thevehicle. Moreover, the presently disclosed embodiments address technicalproblems inherent within computing systems including the difficulty ofprogrammatically extracting physical features from sensor data when twoor more physical features overlap. These technical problems areaddressed by the various technical solutions described herein, includingtechniques for processing sensor data to improve differentiation betweendifferent physical features present in the sensor data. Annotationsperformed based on the processed sensor data may enable the assignmentof more accurate and consistent sematic labels.

As used herein, the term “semantic annotation” is used to denoteinformation that extends beyond sensor data to provide semanticunderstanding of at least a portion of that sensor data, thus providingfor example a meaning or context of the data. Examples of semanticannotations are provided herein, such as information designating aportion of sensor data as a given physical feature. Embodiments of thepresent disclosure may be of use in self-driving vehicles. For thatreason, some examples are provided herein of semantic annotations andphysical features that may be of particular use to self-drivingvehicles, such as crosswalks, traffic lanes, traffic signals, etc.However, embodiments of the present disclosure may additionally oralternatively be used to generate validated semantic understanding forother physical features or objects, such as the identification of cars,people, bicycles, etc.

The term “sensor data,” as used herein, refers to data generated by orgenerally derivable from sensors (e.g., sensors from an autonomoussystem that is the same as or similar to autonomous system 202,described below) that reflect the physical world. For example, sensordata may refer to raw data (e.g., the bits generated by a sensor) or todata points, images, point cloud, electronic map data of a geographicarea, etc., generated from such raw data. As an illustrative example,sensor data may refer to a “ground-level” or “street-level” image, suchas an image directly captured by a camera, a point cloud generated froma LiDAR sensor, a “birds-eye view” (e.g., top-view) image or mapgenerated by movement of a sensor through a geographic area, or thelike. In some instances, semantic annotations may be used to modify suchimages in a manner that identifies features of the images.

The term “geographic data,” as used herein, refers to graph-basedgeographic data (sometimes referred to as geospatial data, georeferenceddata, geoinformation, or geodata) in which traffic lanes are representedas edges (e.g., undirected edges) and intersections between trafficlanes are represented as nodes. Examples of geographic data includeOpenStreetMap (OSM) project data, Google Maps data, HERE map data, orthe like.

Referring now to FIG. 1 , illustrated is example environment 100 inwhich vehicles that include autonomous systems, as well as vehicles thatdo not, are operated. As illustrated, environment 100 includes vehicles102 a-102 n, objects 104 a-104 n, routes 106 a-106 n, area 108,vehicle-to-infrastructure (V2I) device 110, network 112, remoteautonomous vehicle (AV) system 114, fleet management system 116, and V2Isystem 118. Vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device110, network 112, autonomous vehicle (AV) system 114, fleet managementsystem 116, and V2I system 118 interconnect (e.g., establish aconnection to communicate and/or the like) via wired connections,wireless connections, or a combination of wired or wireless connections.In some embodiments, objects 104 a-104 n interconnect with at least oneof vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device 110,network 112, autonomous vehicle (AV) system 114, fleet management system116, and V2I system 118 via wired connections, wireless connections, ora combination of wired or wireless connections.

Vehicles 102 a-102 n (referred to individually as vehicle 102 andcollectively as vehicles 102) include at least one device configured totransport goods and/or people. In some embodiments, vehicles 102 areconfigured to be in communication with V2I device 110, remote AV system114, fleet management system 116, and/or V2I system 118 via network 112.In some embodiments, vehicles 102 include cars, buses, trucks, trains,and/or the like. In some embodiments, vehicles 102 are the same as, orsimilar to, vehicles 200, described herein (see FIG. 2 ). In someembodiments, a vehicle 200 of a set of vehicles 200 is associated withan autonomous fleet manager. In some embodiments, vehicles 102 travelalong respective routes 106 a-106 n (referred to individually as route106 and collectively as routes 106), as described herein. In someembodiments, one or more vehicles 102 include an autonomous system(e.g., an autonomous system that is the same as or similar to autonomoussystem 202).

Objects 104 a-104 n (referred to individually as object 104 andcollectively as objects 104) include, for example, at least one vehicle,at least one pedestrian, at least one cyclist, at least one structure(e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Eachobject 104 is stationary (e.g., located at a fixed location for a periodof time) or mobile (e.g., having a velocity and associated with at leastone trajectory). In some embodiments, objects 104 are associated withcorresponding locations in area 108.

Routes 106 a-106 n (referred to individually as route 106 andcollectively as routes 106) are each associated with (e.g., prescribe) asequence of actions (also known as a trajectory) connecting states alongwhich an AV can navigate. Each route 106 starts at an initial state(e.g., a state that corresponds to a first spatiotemporal location,velocity, and/or the like) and a final goal state (e.g., a state thatcorresponds to a second spatiotemporal location that is different fromthe first spatiotemporal location) or goal region (e.g. a subspace ofacceptable states (e.g., terminal states)). In some embodiments, thefirst state includes a location at which an individual or individualsare to be picked-up by the AV and the second state or region includes alocation or locations at which the individual or individuals picked-upby the AV are to be dropped-off. In some embodiments, routes 106 includea plurality of acceptable state sequences (e.g., a plurality ofspatiotemporal location sequences), the plurality of state sequencesassociated with (e.g., defining) a plurality of trajectories. In anexample, routes 106 include only high level actions or imprecise statelocations, such as a series of connected roads dictating turningdirections at roadway intersections. Additionally, or alternatively,routes 106 may include more precise actions or states such as, forexample, specific target lanes or precise locations within the laneareas and targeted speed at those positions. In an example, routes 106include a plurality of precise state sequences along the at least onehigh level action sequence with a limited lookahead horizon to reachintermediate goals, where the combination of successive iterations oflimited horizon state sequences cumulatively correspond to a pluralityof trajectories that collectively form the high level route to terminateat the final goal state or region.

Area 108 includes a physical area (e.g., a geographic region) withinwhich vehicles 102 can navigate. In an example, area 108 includes atleast one state (e.g., a country, a province, an individual state of aplurality of states included in a country, etc.), at least one portionof a state, at least one city, at least one portion of a city, etc. Insome embodiments, area 108 includes at least one named thoroughfare(referred to herein as a “road”) such as a highway, an interstatehighway, a parkway, a city street, etc. Additionally, or alternatively,in some examples area 108 includes at least one unnamed road such as adriveway, a section of a parking lot, a section of a vacant and/orundeveloped lot, a dirt path, etc. In some embodiments, a road includesat least one lane (e.g., a portion of the road that can be traversed byvehicles 102). In an example, a road includes at least one laneassociated with (e.g., identified based on) at least one lane marking.

Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as aVehicle-to-Infrastructure (V2X) device) includes at least one deviceconfigured to be in communication with vehicles 102 and/or V2Iinfrastructure system 118. In some embodiments, V2I device 110 isconfigured to be in communication with vehicles 102, remote AV system114, fleet management system 116, and/or V2I system 118 via network 112.In some embodiments, V2I device 110 includes a radio frequencyidentification (RFID) device, signage, cameras (e.g., two-dimensional(2D) and/or three-dimensional (3D) cameras), lane markers, streetlights,parking meters, etc. In some embodiments, V2I device 110 is configuredto communicate directly with vehicles 102. Additionally, oralternatively, in some embodiments V2I device 110 is configured tocommunicate with vehicles 102, remote AV system 114, and/or fleetmanagement system 116 via V2I system 118. In some embodiments, V2Idevice 110 is configured to communicate with V2I system 118 via network112.

Network 112 includes one or more wired and/or wireless networks. In anexample, network 112 includes a cellular network (e.g., a long termevolution (LTE) network, a third generation (3G) network, a fourthgeneration (4G) network, a fifth generation (5G) network, a codedivision multiple access (CDMA) network, etc.), a public land mobilenetwork (PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the publicswitched telephone network (PSTN), a private network, an ad hoc network,an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, etc., a combination of some or all of these networks,and/or the like.

Remote AV system 114 includes at least one device configured to be incommunication with vehicles 102, V2I device 110, network 112, remote AVsystem 114, fleet management system 116, and/or V2I system 118 vianetwork 112. In an example, remote AV system 114 includes a server, agroup of servers, and/or other like devices. In some embodiments, remoteAV system 114 is co-located with the fleet management system 116. Insome embodiments, remote AV system 114 is involved in the installationof some or all of the components of a vehicle, including an autonomoussystem, an autonomous vehicle compute, software implemented by anautonomous vehicle compute, and/or the like. In some embodiments, remoteAV system 114 maintains (e.g., updates and/or replaces) such componentsand/or software during the lifetime of the vehicle.

Fleet management system 116 includes at least one device configured tobe in communication with vehicles 102, V2I device 110, remote AV system114, and/or V2I infrastructure system 118. In an example, fleetmanagement system 116 includes a server, a group of servers, and/orother like devices. In some embodiments, fleet management system 116 isassociated with a ridesharing company (e.g., an organization thatcontrols operation of multiple vehicles (e.g., vehicles that includeautonomous systems and/or vehicles that do not include autonomoussystems) and/or the like).

In some embodiments, V2I system 118 includes at least one deviceconfigured to be in communication with vehicles 102, V2I device 110,remote AV system 114, and/or fleet management system 116 via network112. In some examples, V2I system 118 is configured to be incommunication with V2I device 110 via a connection different fromnetwork 112. In some embodiments, V2I system 118 includes a server, agroup of servers, and/or other like devices. In some embodiments, V2Isystem 118 is associated with a municipality or a private institution(e.g., a private institution that maintains V2I device 110 and/or thelike).

The number and arrangement of elements illustrated in FIG. 1 areprovided as an example. There can be additional elements, fewerelements, different elements, and/or differently arranged elements, thanthose illustrated in FIG. 1 . Additionally, or alternatively, at leastone element of environment 100 can perform one or more functionsdescribed as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements ofenvironment 100 can perform one or more functions described as beingperformed by at least one different set of elements of environment 100.

Referring now to FIG. 2 , vehicle 200 includes autonomous system 202,powertrain control system 204, steering control system 206, and brakesystem 208. In some embodiments, vehicle 200 is the same as or similarto vehicle 102 (see FIG. 1 ). In some embodiments, vehicle 102 haveautonomous capability (e.g., implement at least one function, feature,device, and/or the like that enable vehicle 200 to be partially or fullyoperated without human intervention including, without limitation, fullyautonomous vehicles (e.g., vehicles that forego reliance on humanintervention), highly autonomous vehicles (e.g., vehicles that foregoreliance on human intervention in certain situations), and/or the like).For a detailed description of fully autonomous vehicles and highlyautonomous vehicles, reference may be made to SAE International'sstandard J3016: Taxonomy and Definitions for Terms Related to On-RoadMotor Vehicle Automated Driving Systems, which is incorporated byreference in its entirety. In some embodiments, vehicle 200 isassociated with an autonomous fleet manager and/or a ridesharingcompany.

Autonomous system 202 includes a sensor suite that includes one or moredevices such as cameras 202 a, LiDAR sensors 202 b, radar sensors 202 c,and microphones 202 d. In some embodiments, autonomous system 202 caninclude more or fewer devices and/or different devices (e.g., ultrasonicsensors, inertial sensors, GPS receivers (discussed below), odometrysensors that generate data associated with an indication of a distancethat vehicle 200 has traveled, and/or the like). In some embodiments,autonomous system 202 uses the one or more devices included inautonomous system 202 to generate data associated with environment 100,described herein. The data generated by the one or more devices ofautonomous system 202 can be used by one or more systems describedherein to observe the environment (e.g., environment 100) in whichvehicle 200 is located. In some embodiments, autonomous system 202includes communication device 202 e, autonomous vehicle computer 202 f,and drive-by-wire (DBW) system 202 h.

Cameras 202 a include at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecomputer 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Cameras 202 ainclude at least one camera (e.g., a digital camera using a light sensorsuch as a charge-coupled device (CCD), a thermal camera, an infrared(IR) camera, an event camera, and/or the like) to capture imagesincluding physical objects (e.g., cars, buses, curbs, people, and/or thelike). In some embodiments, camera 202 a generates camera data asoutput. In some examples, camera 202 a generates camera data thatincludes image data associated with an image. In this example, the imagedata may specify at least one parameter (e.g., image characteristicssuch as exposure, brightness, etc., an image timestamp, and/or the like)corresponding to the image. In such an example, the image may be in aformat (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments,camera 202 a includes a plurality of independent cameras configured on(e.g., positioned on) a vehicle to capture images for the purpose ofstereopsis (stereo vision). In some examples, camera 202 a includes aplurality of cameras that generate image data and transmit the imagedata to autonomous vehicle computer 202 f and/or a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ). In such an example,autonomous vehicle computer 202 f determines depth to one or moreobjects in a field of view of at least two cameras of the plurality ofcameras based on the image data from the at least two cameras. In someembodiments, cameras 202 a is configured to capture images of objectswithin a distance from cameras 202 a (e.g., up to 100 meters, up to akilometer, and/or the like). Accordingly, cameras 202 a include featuressuch as sensors and lenses that are optimized for perceiving objectsthat are at one or more distances from cameras 202 a.

In an embodiment, camera 202 a includes at least one camera configuredto capture one or more images associated with one or more trafficlights, street signs and/or other physical objects that provide visualnavigation information. In some embodiments, camera 202 a generatestraffic light data associated with one or more images. In some examples,camera 202 a generates TLD data associated with one or more images thatinclude a format (e.g., RAW, JPEG, PNG, and/or the like). In someembodiments, camera 202 a that generates TLD data differs from othersystems described herein incorporating cameras in that camera 202 a caninclude one or more cameras with a wide field of view (e.g., awide-angle lens, a fish-eye lens, a lens having a viewing angle ofapproximately 120 degrees or more, and/or the like) to generate imagesabout as many physical objects as possible.

Laser Detection and Ranging (LiDAR) sensors 202 b include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle computer 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202 b include a system configured to transmit lightfrom a light emitter (e.g., a laser transmitter). Light emitted by LiDARsensors 202 b include light (e.g., infrared light and/or the like) thatis outside of the visible spectrum. In some embodiments, duringoperation, light emitted by LiDAR sensors 202 b encounters a physicalobject (e.g., a vehicle) and is reflected back to LiDAR sensors 202 b.In some embodiments, the light emitted by LiDAR sensors 202 b does notpenetrate the physical objects that the light encounters. LiDAR sensors202 b also include at least one light detector which detects the lightthat was emitted from the light emitter after the light encounters aphysical object. In some embodiments, at least one data processingsystem associated with LiDAR sensors 202 b generates an image (e.g., apoint cloud, a combined point cloud, and/or the like) representing theobjects included in a field of view of LiDAR sensors 202 b. In someexamples, the at least one data processing system associated with LiDARsensor 202 b generates an image that represents the boundaries of aphysical object, the surfaces (e.g., the topology of the surfaces) ofthe physical object, and/or the like. In such an example, the image isused to determine the boundaries of physical objects in the field ofview of LiDAR sensors 202 b.

Radio Detection and Ranging (radar) sensors 202 c include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle computer 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Radar sensors 202 c include a system configured to transmit radiowaves (either pulsed or continuously). The radio waves transmitted byradar sensors 202 c include radio waves that are within a predeterminedspectrum In some embodiments, during operation, radio waves transmittedby radar sensors 202 c encounter a physical object and are reflectedback to radar sensors 202 c. In some embodiments, the radio wavestransmitted by radar sensors 202 c are not reflected by some objects. Insome embodiments, at least one data processing system associated withradar sensors 202 c generates signals representing the objects includedin a field of view of radar sensors 202 c. For example, the at least onedata processing system associated with radar sensor 202 c generates animage that represents the boundaries of a physical object, the surfaces(e.g., the topology of the surfaces) of the physical object, and/or thelike. In some examples, the image is used to determine the boundaries ofphysical objects in the field of view of radar sensors 202 c.

Microphones 202 d includes at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecomputer 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Microphones 202 dinclude one or more microphones (e.g., array microphones, externalmicrophones, and/or the like) that capture audio signals and generatedata associated with (e.g., representing) the audio signals. In someexamples, microphones 202 d include transducer devices and/or likedevices. In some embodiments, one or more systems described herein canreceive the data generated by microphones 202 d and determine a positionof an object relative to vehicle 200 (e.g., a distance and/or the like)based on the audio signals associated with the data.

Communication device 202 e include at least one device configured to bein communication with cameras 202 a, LiDAR sensors 202 b, radar sensors202 c, microphones 202 d, autonomous vehicle computer 202 f, safetycontroller 202 g, and/or DBW system 202 h. For example, communicationdevice 202 e may include a device that is the same as or similar tocommunication interface 314 of FIG. 3 . In some embodiments,communication device 202 e includes a vehicle-to-vehicle (V2V)communication device (e.g., a device that enables wireless communicationof data between vehicles).

Autonomous vehicle computer 202 f include at least one device configuredto be in communication with cameras 202 a, LiDAR sensors 202 b, radarsensors 202 c, microphones 202 d, communication device 202 e, safetycontroller 202 g, and/or DBW system 202 h. In some examples, autonomousvehicle computer 202 f includes a device such as a client device, amobile device (e.g., a cellular telephone, a tablet, and/or the like) aserver (e.g., a computing device including one or more centralprocessing units, graphical processing units, and/or the like), and/orthe like. In some embodiments, autonomous vehicle computer 202 f is thesame as or similar to autonomous vehicle computer 400, described herein.Additionally, or alternatively, in some embodiments autonomous vehiclecomputer 202 f is configured to be in communication with an autonomousvehicle system (e.g., an autonomous vehicle system that is the same asor similar to remote AV system 114 of FIG. 1 ), a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2Idevice that is the same as or similar to V2I device 110 of FIG. 1 ),and/or a V2I system (e.g., a V2I system that is the same as or similarto V2I system 118 of FIG. 1 ).

Safety controller 202 g includes at least one device configured to be incommunication with cameras 202 a, LiDAR sensors 202 b, radar sensors 202c, microphones 202 d, communication device 202 e, autonomous vehiclecomputer 202 f, and/or DBW system 202 h. In some examples, safetycontroller 202 g includes one or more controllers (electricalcontrollers, electromechanical controllers, and/or the like) that areconfigured to generate and/or transmit control signals to operate one ormore devices of vehicle 200 (e.g., powertrain control system 204,steering control system 206, brake system 208, and/or the like). In someembodiments, safety controller 202 g is configured to generate controlsignals that take precedence over (e.g., overrides) control signalsgenerated and/or transmitted by autonomous vehicle computer 202 f.

DBW system 202 h includes at least one device configured to be incommunication with communication device 202 e and/or autonomous vehiclecomputer 202 f. In some examples, DBW system 202 h includes one or morecontrollers (e.g., electrical controllers, electromechanicalcontrollers, and/or the like) that are configured to generate and/ortransmit control signals to operate one or more devices of vehicle 200(e.g., powertrain control system 204, steering control system 206, brakesystem 208, and/or the like). Additionally, or alternatively, the one ormore controllers of DBW system 202 h are configured to generate and/ortransmit control signals to operate at least one different device (e.g.,a turn signal, headlights, door locks, windshield wipers, and/or thelike) of vehicle 200.

Powertrain control system 204 includes at least one device configured tobe in communication with DBW system 202 h. In some examples, powertraincontrol system 204 includes at least one controller, actuator, and/orthe like. In some embodiments, powertrain control system 204 receivescontrol signals from DBW system 202 h and powertrain control system 204causes vehicle 200 to start moving forward, stop moving forward, startmoving backward, stop moving backward, accelerate in a direction,decelerate in a direction, perform a left turn, perform a right turn,and/or the like. In an example, powertrain control system 204 causes theenergy (e.g., fuel, electricity, and/or the like) provided to a motor ofthe vehicle to increase, remain the same, or decrease, thereby causingat least one wheel of vehicle 200 to rotate or not rotate.

Steering control system 206 includes at least one device configured torotate one or more wheels of vehicle 200. In some examples, steeringcontrol system 206 includes at least one controller, actuator, and/orthe like. In some embodiments, steering control system 206 causes thefront two wheels and/or the rear two wheels of vehicle 200 to rotate tothe left or right to cause vehicle 200 to turn to the left or right.

Brake system 208 includes at least one device configured to actuate oneor more brakes to cause vehicle 200 to reduce speed and/or remainstationary. In some examples, brake system 208 includes at least onecontroller and/or actuator that is configured to cause one or morecalipers associated with one or more wheels of vehicle 200 to close on acorresponding rotor of vehicle 200. Additionally, or alternatively, insome examples brake system 208 includes an automatic emergency braking(AEB) system, a regenerative braking system, and/or the like.

In some embodiments, vehicle 200 includes at least one platform sensor(not explicitly illustrated) that measures or infers properties of astate or a condition of vehicle 200. In some examples, vehicle 200includes platform sensors such as a global positioning system (GPS)receiver, an inertial measurement unit (IMU), a wheel speed sensor, awheel brake pressure sensor, a wheel torque sensor, an engine torquesensor, a steering angle sensor, and/or the like.

Referring now to FIG. 3 , illustrated is a schematic diagram of a device300. As illustrated, device 300 includes processor 304, memory 306,storage component 308, input interface 310, output interface 312,communication interface 314, and bus 302. In some embodiments, device300 corresponds to at least one device of vehicles 102 (e.g., at leastone device of a system of vehicles 102), at least one device ofvehicle-to-infrastructure (V2I) device 110, and/or one or more devicesof network 112 (e.g., one or more devices of a system of network 112).In some embodiments, one or more devices of vehicles 102 (e.g., one ormore devices of a system of vehicles 102), and/or one or more devices ofnetwork 112 (e.g., one or more devices of a system of network 112)include at least one device 300 and/or at least one component of device300. As shown in FIG. 3 , device 300 includes bus 302, processor 304,memory 306, storage component 308, input interface 310, output interface312, and communication interface 314.

Bus 302 includes a component that permits communication among thecomponents of device 300. In some embodiments, processor 304 isimplemented in hardware, software, or a combination of hardware andsoftware. In some examples, processor 304 includes a processor (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), and/or the like), a microphone, adigital signal processor (DSP), and/or any processing component (e.g., afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), and/or the like) that can be programmed to perform atleast one function. Memory 306 includes random access memory (RAM),read-only memory (ROM), and/or another type of dynamic and/or staticstorage device (e.g., flash memory, magnetic memory, optical memory,and/or the like) that stores data and/or instructions for use byprocessor 304.

Storage component 308 stores data and/or software related to theoperation and use of device 300. In some examples, storage component 308includes a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, and/or the like), a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/oranother type of computer readable medium, along with a correspondingdrive.

Input interface 310 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touchscreendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, a camera, and/or the like). Additionally or alternatively,in some embodiments input interface 310 includes a sensor that sensesinformation (e.g., a global positioning system (GPS) receiver, anaccelerometer, a gyroscope, an actuator, and/or the like). Outputinterface 312 includes a component that provides output information fromdevice 300 (e.g., a display, a speaker, one or more light-emittingdiodes (LEDs), and/or the like).

In some embodiments, communication interface 314 includes atransceiver-like component (e.g., a transceiver, a separate receiver andtransmitter, and/or the like) that permits device 300 to communicatewith other devices via a wired connection, a wireless connection, or acombination of wired and wireless connections. In some examples,communication interface 314 permits device 300 to receive informationfrom another device and/or provide information to another device. Insome examples, communication interface 314 includes an Ethernetinterface, an optical interface, a coaxial interface, an infraredinterface, a radio frequency (RF) interface, a universal serial bus(USB) interface, a WiFi® interface, a cellular network interface, and/orthe like.

In some embodiments, device 300 performs one or more processes describedherein. Device 300 performs these processes based on processor 304executing software instructions stored by a computer-readable medium,such as memory 305 and/or storage component 308. A computer-readablemedium (e.g., a non-transitory computer readable medium) is definedherein as a non-transitory memory device. A non-transitory memory deviceincludes memory space located inside a single physical storage device ormemory space spread across multiple physical storage devices.

In some embodiments, software instructions are read into memory 306and/or storage component 308 from another computer-readable medium orfrom another device via communication interface 314. When executed,software instructions stored in memory 306 and/or storage component 308cause processor 304 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry is used in place ofor in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and softwareunless explicitly stated otherwise.

Memory 306 and/or storage component 308 includes data storage or atleast one data structure (e.g., a database and/or the like). Device 300is capable of receiving information from, storing information in,communicating information to, or searching information stored in thedata storage or the at least one data structure in memory 306 or storagecomponent 308. In some examples, the information includes network data,input data, output data, or any combination thereof.

In some embodiments, device 300 is configured to execute softwareinstructions that are either stored in memory 306 and/or in the memoryof another device (e.g., another device that is the same as or similarto device 300). As used herein, the term “module” refers to at least oneinstruction stored in memory 306 and/or in the memory of another devicethat, when executed by processor 304 and/or by a processor of anotherdevice (e.g., another device that is the same as or similar to device300) cause device 300 (e.g., at least one component of device 300) toperform one or more processes described herein. In some embodiments, amodule is implemented in software, firmware, hardware, and/or the like.

The number and arrangement of components illustrated in FIG. 3 areprovided as an example. In some embodiments, device 300 can includeadditional components, fewer components, different components, ordifferently arranged components than those illustrated in FIG. 3 .Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 300 can perform one or more functions described asbeing performed by another component or another set of components ofdevice 300.

Referring now to FIG. 4A, illustrated is an example block diagram of anautonomous vehicle computer 400 (sometimes referred to as an “AVstack”). As illustrated, autonomous vehicle computer 400 includesperception system 402 (sometimes referred to as a perception module),planning system 404 (sometimes referred to as a planning module),localization system 406 (sometimes referred to as a localizationmodule), control system 408 (sometimes referred to as a control module),and database 410. In some embodiments, perception system 402, planningsystem 404, localization system 406, control system 408, and database410 are included and/or implemented in an autonomous navigation systemof a vehicle (e.g., autonomous vehicle computer 202 f of vehicle 200).Additionally, or alternatively, in some embodiments perception system402, planning system 404, localization system 406, control system 408,and database 410 are included in one or more standalone systems (e.g.,one or more systems that are the same as or similar to autonomousvehicle computer 400 and/or the like). In some examples, perceptionsystem 402, planning system 404, localization system 406, control system408, and database 410 are included in one or more standalone systemsthat are located in a vehicle and/or at least one remote system asdescribed herein. In some embodiments, any and/or all of the systemsincluded in autonomous vehicle computer 400 are implemented in software(e.g., in software instructions stored in memory), computer hardware(e.g., by microprocessors, microcontrollers, application-specificintegrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs),and/or the like), or combinations of computer software and computerhardware. It will also be understood that, in some embodiments,autonomous vehicle computer 400 is configured to be in communicationwith a remote system (e.g., an autonomous vehicle system that is thesame as or similar to remote AV system 114, a fleet management system116 that is the same as or similar to fleet management system 116, a V2Isystem that is the same as or similar to V2I system 118, and/or thelike).

In some embodiments, perception system 402 receives data associated withat least one physical object (e.g., data that is used by perceptionsystem 402 to detect the at least one physical object) in an environmentand classifies the at least one physical object. In some examples,perception system 402 receives image data captured by at least onecamera (e.g., cameras 202 a), the image associated with (e.g.,representing) one or more physical objects within a field of view of theat least one camera. In such an example, perception system 402classifies at least one physical object based on one or more groupingsof physical objects (e.g., bicycles, vehicles, traffic signs,pedestrians, and/or the like). In some embodiments, perception system402 transmits data associated with the classification of the physicalobjects to planning system 404 based on perception system 402classifying the physical objects.

In some embodiments, planning system 404 receives data associated with adestination and generates data associated with at least one route (e.g.,routes 106) along which a vehicle (e.g., vehicles 102) can travel alongtoward a destination. In some embodiments, planning system 404periodically or continuously receives data from perception system 402(e.g., data associated with the classification of physical objects,described above) and planning system 404 updates the at least onetrajectory or generates at least one different trajectory based on thedata generated by perception system 402. In some embodiments, planningsystem 404 receives data associated with an updated position of avehicle (e.g., vehicles 102) from localization system 406 and planningsystem 404 updates the at least one trajectory or generates at least onedifferent trajectory based on the data generated by localization system406.

In some embodiments, localization system 406 receives data associatedwith (e.g., representing) a location of a vehicle (e.g., vehicles 102)in an area. In some examples, localization system 406 receives LiDARdata associated with at least one point cloud generated by at least oneLiDAR sensor (e.g., LiDAR sensors 202 b). In certain examples,localization system 406 receives data associated with at least one pointcloud from multiple LiDAR sensors and localization system 406 generatesa combined point cloud based on each of the point clouds. In theseexamples, localization system 406 compares the at least one point cloudor the combined point cloud to two-dimensional (2D) and/or athree-dimensional (3D) map of the area stored in database 410.Localization system 406 then determines the position of the vehicle inthe area based on localization system 406 comparing the at least onepoint cloud or the combined point cloud to the map. In some embodiments,the map includes a combined point cloud of the area generated prior tonavigation of the vehicle. In some embodiments, maps include, withoutlimitation, high-precision maps of the roadway geometric properties,maps describing road network connectivity properties, maps describingroadway physical properties (such as traffic speed, traffic volume, thenumber of vehicular and cyclist traffic lanes, lane width, lane trafficdirections, or lane marker types and locations, or combinationsthereof), and maps describing the spatial locations of road featuressuch as crosswalks, traffic signs or other travel signals of varioustypes. In some embodiments, the map is generated in real-time based onthe data received by the perception system.

In another example, localization system 406 receives Global NavigationSatellite System (GNSS) data generated by a global positioning system(GPS) receiver. In some examples, localization system 406 receives GNSSdata associated with the location of the vehicle in the area andlocalization system 406 determines a latitude and longitude of thevehicle in the area. In such an example, localization system 406determines the position of the vehicle in the area based on the latitudeand longitude of the vehicle. In some embodiments, localization system406 generates data associated with the position of the vehicle. In someexamples, localization system 406 generates data associated with theposition of the vehicle based on localization system 406 determining theposition of the vehicle. In such an example, the data associated withthe position of the vehicle includes data associated with one or moresemantic properties corresponding to the position of the vehicle.

In some embodiments, control system 408 receives data associated with atleast one trajectory from planning system 404 and control system 408controls operation of the vehicle. In some examples, control system 408receives data associated with at least one trajectory from planningsystem 404 and control system 408 controls operation of the vehicle bygenerating and transmitting control signals to cause a powertraincontrol system (e.g., DBW system 202 h, powertrain control system 204,and/or the like), a steering control system (e.g., steering controlsystem 206), and/or a brake system (e.g., brake system 208) to operate.In an example, where a trajectory includes a left turn, control system408 transmits a control signal to cause steering control system 206 toadjust a steering angle of vehicle 200, thereby causing vehicle 200 toturn left. Additionally, or alternatively, control system 408 generatesand transmits control signals to cause other devices (e.g., headlights,turn signal, door locks, windshield wipers, and/or the like) of vehicle200 to change states.

In some embodiments, perception system 402, planning system 404,localization system 406, and/or control system 408 implement at leastone machine learning model (e.g., at least one multilayer perceptron(MLP), at least one convolutional neural network (CNN), at least onerecurrent neural network (RNN), at least one autoencoder, at least onetransformer, and/or the like). In some examples, perception system 402,planning system 404, localization system 406, and/or control system 408implement at least one machine learning model alone or in combinationwith one or more of the above-noted systems. In some examples,perception system 402, planning system 404, localization system 406,and/or control system 408 implement at least one machine learning modelas part of a pipeline (e.g., a pipeline for identifying one or moreobjects located in an environment and/or the like). An example of animplementation of a machine learning model is included below withrespect to FIGS. 4B-4D.

Database 410 stores data that is transmitted to, received from, and/orupdated by perception system 402, planning system 404, localizationsystem 406 and/or control system 408. In some examples, database 410includes a storage component (e.g., a storage component that is the sameas or similar to storage component 308 of FIG. 3 ) that stores dataand/or software related to the operation and uses at least one system ofautonomous vehicle computer 400. In some embodiments, database 410stores data associated with 2D and/or 3D maps of at least one area. Insome examples, database 410 stores data associated with 2D and/or 3Dmaps of a portion of a city, multiple portions of multiple cities,multiple cities, a county, a state, a State (e.g., a country), and/orthe like). In such an example, a vehicle (e.g., a vehicle that is thesame as or similar to vehicles 102 and/or vehicle 200) can drive alongone or more drivable regions (e.g., single-lane roads, multi-lane roads,highways, back roads, off road trails, and/or the like) and cause atleast one LiDAR sensor (e.g., a LiDAR sensor that is the same as orsimilar to LiDAR sensors 202 b) to generate data associated with animage representing the objects included in a field of view of the atleast one LiDAR sensor.

In some embodiments, database 410 can be implemented across a pluralityof devices. In some examples, database 410 is included in a vehicle(e.g., a vehicle that is the same as or similar to vehicles 102 and/orvehicle 200), an autonomous vehicle system (e.g., an autonomous vehiclesystem that is the same as or similar to remote AV system 114, a fleetmanagement system (e.g., a fleet management system that is the same asor similar to fleet management system 116 of FIG. 1 , a V2I system(e.g., a V2I system that is the same as or similar to V2I system 118 ofFIG. 1 ) and/or the like.

Referring now to FIG. 4B, illustrated is a diagram of an implementationof a machine learning model. More specifically, illustrated is a diagramof an implementation of a convolutional neural network (CNN) 420. Forpurposes of illustration, the following description of CNN 420 will bewith respect to an implementation of CNN 420 by perception system 402.However, it will be understood that in some examples CNN 420 (e.g., oneor more components of CNN 420) is implemented by other systems differentfrom, or in addition to, perception system 402 such as planning system404, localization system 406, and/or control system 408. While CNN 420includes certain features as described herein, these features areprovided for the purpose of illustration and are not intended to limitthe present disclosure.

CNN 420 includes a plurality of convolution layers including firstconvolution layer 422, second convolution layer 424, and convolutionlayer 426. In some embodiments, CNN 420 includes sub-sampling layer 428(sometimes referred to as a pooling layer). In some embodiments,sub-sampling layer 428 and/or other subsampling layers have a dimension(i.e., an amount of nodes) that is less than a dimension of an upstreamsystem. By virtue of sub-sampling layer 428 having a dimension that isless than a dimension of an upstream layer, CNN 420 consolidates theamount of data associated with the initial input and/or the output of anupstream layer to thereby decrease the amount of computations necessaryfor CNN 420 to perform downstream convolution operations. Additionally,or alternatively, by virtue of sub-sampling layer 428 being associatedwith (e.g., configured to perform) at least one subsampling function (asdescribed below with respect to FIGS. 4C and 4D), CNN 420 consolidatesthe amount of data associated with the initial input.

Perception system 402 performs convolution operations based onperception system 402 providing respective inputs and/or outputsassociated with each of first convolution layer 422, second convolutionlayer 424, and convolution layer 426 to generate respective outputs. Insome examples, perception system 402 implements CNN 420 based onperception system 402 providing data as input to first convolution layer422, second convolution layer 424, and convolution layer 426. In such anexample, perception system 402 provides the data as input to firstconvolution layer 422, second convolution layer 424, and convolutionlayer 426 based on perception system 402 receiving data from one or moredifferent systems (e.g., one or more systems of a vehicle that is thesame as or similar to vehicle 102), a remote AV system that is the sameas or similar to remote AV system 114, a fleet management system that isthe same as or similar to fleet management system 116, a V2I system thatis the same as or similar to V2I system 118, and/or the like). Adetailed description of convolution operations is included below withrespect to FIG. 4C.

In some embodiments, perception system 402 provides data associated withan input (referred to as an initial input) to first convolution layer422 and perception system 402 generates data associated with an outputusing first convolution layer 422. In some embodiments, perceptionsystem 402 provides an output generated by a convolution layer as inputto a different convolution layer. For example, perception system 402provides the output of first convolution layer 422 as input tosub-sampling layer 428, second convolution layer 424, and/or convolutionlayer 426. In such an example, first convolution layer 422 is referredto as an upstream layer and sub-sampling layer 428, second convolutionlayer 424, and/or convolution layer 426 are referred to as downstreamlayers. Similarly, in some embodiments perception system 402 providesthe output of sub-sampling layer 428 to second convolution layer 424and/or convolution layer 426 and, in this example, sub-sampling layer428 would be referred to as an upstream layer and second convolutionlayer 424 and/or convolution layer 426 would be referred to asdownstream layers.

In some embodiments, perception system 402 processes the data associatedwith the input provided to CNN 420 before perception system 402 providesthe input to CNN 420. For example, perception system 402 processes thedata associated with the input provided to CNN 420 based on perceptionsystem 402 normalizing sensor data (e.g., image data, LiDAR data, radardata, and/or the like).

In some embodiments, CNN 420 generates an output based on perceptionsystem 402 performing convolution operations associated with eachconvolution layer. In some examples, CNN 420 generates an output basedon perception system 402 performing convolution operations associatedwith each convolution layer and an initial input. In some embodiments,perception system 402 generates the output and provides the output asfully connected layer 430. In some examples, perception system 402provides the output of convolution layer 426 as fully connected layer430, where fully connected layer 420 includes data associated with aplurality of feature values referred to as F1, F2 . . . FN. In thisexample, the output of convolution layer 426 includes data associatedwith a plurality of output feature values that represent a prediction.

In some embodiments, perception system 402 identifies a prediction fromamong a plurality of predictions based on perception system 402identifying a feature value that is associated with the highestlikelihood of being the correct prediction from among the plurality ofpredictions. For example, where fully connected layer 430 includesfeature values F1, F2, . . . FN, and F1 is the greatest feature value,perception system 402 identifies the prediction associated with F1 asbeing the correct prediction from among the plurality of predictions. Insome embodiments, perception system 402 trains CNN 420 to generate theprediction. In some examples, perception system 402 trains CNN 420 togenerate the prediction based on perception system 402 providingtraining data associated with the prediction to CNN 420.

Referring now to FIGS. 4C and 4D, illustrated is a diagram of exampleoperation of CNN 440 by perception system 402. In some embodiments, CNN440 (e.g., one or more components of CNN 440) is the same as, or similarto, CNN 420 (e.g., one or more components of CNN 420) (see FIG. 4B).

At step 450, perception system 402 provides data associated with animage as input to CNN 440 (step 450). For example, as illustrated,perception system 402 provides the data associated with the image to CNN440, where the image is a greyscale image represented as values storedin a two-dimensional (2D) array. In some embodiments, the dataassociated with the image may include data associated with a colorimage, the color image represented as values stored in athree-dimensional (3D) array. Additionally, or alternatively, the dataassociated with the image may include data associated with an infraredimage, a radar image, and/or the like.

At step 455, CNN 440 performs a first convolution function. For example,CNN 440 performs the first convolution function based on CNN 440providing the values representing the image as input to one or moreneurons (not explicitly illustrated) included in first convolution layer442. In this example, the values representing the image can correspondto values representing a region of the image (sometimes referred to as areceptive field). In some embodiments, each neuron is associated with afilter (not explicitly illustrated). A filter (sometimes referred to asa kernel) is representable as an array of values that corresponds insize to the values provided as input to the neuron. In one example, afilter may be configured to identify edges (e.g., horizontal lines,vertical lines, straight lines, and/or the like). In successiveconvolution layers, the filters associated with neurons may beconfigured to identify successively more complex patterns (e.g., arcs,objects, and/or the like).

In some embodiments, CNN 440 performs the first convolution functionbased on CNN 440 multiplying the values provided as input to each of theone or more neurons included in first convolution layer 442 with thevalues of the filter that corresponds to each of the one or moreneurons. For example, CNN 440 can multiply the values provided as inputto each of the one or more neurons included in first convolution layer442 with the values of the filter that corresponds to each of the one ormore neurons to generate a single value or an array of values as anoutput. In some embodiments, the collective output of the neurons offirst convolution layer 442 is referred to as a convolved output. Insome embodiments, where each neuron has the same filter, the convolvedoutput is referred to as a feature map.

In some embodiments, CNN 440 provides the outputs of each neuron offirst convolutional layer 442 to neurons of a downstream layer. Forpurposes of clarity, an upstream layer can be a layer that transmitsdata to a different layer (referred to as a downstream layer). Forexample, CNN 440 can provide the outputs of each neuron of firstconvolutional layer 442 to corresponding neurons of a subsampling layer.In an example, CNN 440 provides the outputs of each neuron of firstconvolutional layer 442 to corresponding neurons of first subsamplinglayer 444. In some embodiments, CNN 440 adds a bias value to theaggregates of all the values provided to each neuron of the downstreamlayer. For example, CNN 440 adds a bias value to the aggregates of allthe values provided to each neuron of first subsampling layer 444. Insuch an example, CNN 440 determines a final value to provide to eachneuron of first subsampling layer 444 based on the aggregates of all thevalues provided to each neuron and an activation function associatedwith each neuron of first subsampling layer 444.

At step 460, CNN 440 performs a first subsampling function. For example,CNN 440 can perform a first subsampling function based on CNN 440providing the values output by first convolution layer 442 tocorresponding neurons of first subsampling layer 444. In someembodiments, CNN 440 performs the first subsampling function based on anaggregation function. In an example, CNN 440 performs the firstsubsampling function based on CNN 440 determining the maximum inputamong the values provided to a given neuron (referred to as a maxpooling function). In another example, CNN 440 performs the firstsubsampling function based on CNN 440 determining the average inputamong the values provided to a given neuron (referred to as an averagepooling function). In some embodiments, CNN 440 generates an outputbased on CNN 440 providing the values to each neuron of firstsubsampling layer 444, the output sometimes referred to as a subsampledconvolved output.

At step 465, CNN 440 performs a second convolution function. In someembodiments, CNN 440 performs the second convolution function in amanner similar to how CNN 440 performed the first convolution function,described above. In some embodiments, CNN 440 performs the secondconvolution function based on CNN 440 providing the values output byfirst subsampling layer 444 as input to one or more neurons (notexplicitly illustrated) included in second convolution layer 446. Insome embodiments, each neuron of second convolution layer 446 isassociated with a filter, as described above. The filter(s) associatedwith second convolution layer 446 may be configured to identify morecomplex patterns than the filter associated with first convolution layer442, as described above.

In some embodiments, CNN 440 performs the second convolution functionbased on CNN 440 multiplying the values provided as input to each of theone or more neurons included in second convolution layer 446 with thevalues of the filter that corresponds to each of the one or moreneurons. For example, CNN 440 can multiply the values provided as inputto each of the one or more neurons included in second convolution layer446 with the values of the filter that corresponds to each of the one ormore neurons to generate a single value or an array of values as anoutput.

In some embodiments, CNN 440 provides the outputs of each neuron ofsecond convolutional layer 446 to neurons of a downstream layer. Forexample, CNN 440 can provide the outputs of each neuron of firstconvolutional layer 442 to corresponding neurons of a subsampling layer.In an example, CNN 440 provides the outputs of each neuron of firstconvolutional layer 442 to corresponding neurons of second subsamplinglayer 448. In some embodiments, CNN 440 adds a bias value to theaggregates of all the values provided to each neuron of the downstreamlayer. For example, CNN 440 adds a bias value to the aggregates of allthe values provided to each neuron of second subsampling layer 448. Insuch an example, CNN 440 determines a final value to provide to eachneuron of second subsampling layer 448 based on the aggregates of allthe values provided to each neuron and an activation function associatedwith each neuron of second subsampling layer 448.

At step 470, CNN 440 performs a second subsampling function. Forexample, CNN 440 can perform a second subsampling function based on CNN440 providing the values output by second convolution layer 446 tocorresponding neurons of second subsampling layer 448. In someembodiments, CNN 440 performs the second subsampling function based onCNN 440 using an aggregation function. In an example, CNN 440 performsthe first subsampling function based on CNN 440 determining the maximuminput or an average input among the values provided to a given neuron,as described above. In some embodiments, CNN 440 generates an outputbased on CNN 440 providing the values to each neuron of secondsubsampling layer 448.

At step 475, CNN 440 provides the output of each neuron of secondsubsampling layer 448 to fully connected layers 449. For example, CNN440 provides the output of each neuron of second subsampling layer 448to fully connected layers 449 to cause fully connected layers 449 togenerate an output. In some embodiments, fully connected layers 449 areconfigured to generate an output associated with a prediction (sometimesreferred to as a classification). The prediction may include anindication that an object included in the image provided as input to CNN440 includes an object, a set of objects, and/or the like. In someembodiments, perception system 402 performs one or more operationsand/or provides the data associated with the prediction to a differentsystem, described herein.

Referring now to FIGS. 5A-5E, illustrated are diagrams of animplementation of a process for semantic annotation, which includesprocessing sensor data to improve differentiation between differentphysical features present in the sensor data.

With reference to FIG. 5A, interactions for generating semantic labelsare illustratively implemented by perception system 402, which as notedabove may be included within vehicle 102. Thus, the interactions of FIG.5A may be used, for example, to provide vehicle 102 with semanticunderstanding of the surrounding geographic area, thus enabling varioussystems (e.g., autonomous vehicle computer 202 f) to perform functionssuch as planning of routes 106 b, determining a location of the vehicle102 within a geographic area, and/or the like.

The interactions of FIG. 5A include passing, to a physical featureextractor 504, sensor data 502 a and geographic data 502 b associatedwith a geographic area. As described above, the sensor data 502 a mayinclude one or more images of the geographic area from which adrivability mask identifying drivable surfaces can be derived.Meanwhile, the geographic data 502 b may include graph-based geographicdata in which edges represent traffic lanes and nodes representintersections between traffic lanes. The sensor data 502 a, which mayinclude one or more images of the geographic area, may be overlap withthe geographic data 502 b. For example, an edge representative of atraffic lane in the geographic data 502 b may correspond to a polygonrepresentative of a drivable surface in the sensor data 502 a. Thephysical feature extractor 504 may extract, based on the sensor data 502a and geographic data 502 b, one or more physical features presentwithin the sensor data 502 a including, for example, traffic lanes,intersections, and/or the like. The physical feature extractor 504 maygenerate a processed image 509 in which each traffic lane is identifiedby the boundary points of a corresponding polygon (e.g., lane polygon).As discussed in more detail below, the physical feature extractor 504may process the sensor data 502 a in order to improve differentiationbetween different physical features present in the sensor data 502,particularly where multiple traffic lanes intersect or merge. Forinstance, the physical feature extractor 504 may perform, based on thesensor data 502 a and/or the geographic data 502 b, one or more oftraffic lane extraction, traffic lane edge filtering, overlappingtraffic lane filtering, intersection extraction, and lane sectionlinking.

Referring again to FIG. 5A, perception system 402 may also include anannotation engine 506 configured to generate semantic labels for thephysical features that are present in the sensor data 502. In someinstances, the semantic annotation may be performed by a trained machinelearning model, such as a neural network, in which case the sensor data502 a may be represented as set of aligned 2-dimensional matrixes, witheach such matrix representing a layer of an image. For example, a colorimage may be represented in 3 channels, each of which corresponding tovalues of a respective primary color that, when combined, result in animage. A greyscale image may be represented as a single matrix, withvalues within the matrix representing the darkness of a pixel in theimage.

In some embodiments, the annotation engine 506 may generate semanticlabels based on the processed image 509 that the physical featureextractor 504 has processed to improve differentiation between differentphysical features. To differentiate between different physical features,the physical feature extractor 504 may add, to the sensor data 502 aand/or geographic data 502 b, one or more additional layers ofinformation. For example, a first layer may be added to the sensor data502 a to indicate whether each location in the matrix (e.g., each“pixel” in the sensor data 502 a) corresponds to an intersection (e.g.,via concatenation of an image showing nodes in a graph of roads), asecond layer may be added to indicate whether each location correspondsto a traffic lane (e.g., via concatenation of an image showing edges inthe graph), a third layer may be added that indicates whether eachlocation corresponds to a crosswalk, and/or the like.

FIG. 5B depicts a diagram illustrating an example process of trafficlane extraction from sensor data 502 a and/or geographic data 502 b. Asshown, sensor data 502 a and geographic data 502 b may be processed by aprocessing system (e.g., physical feature extractor 504 of perceptionsystem 402) to generate a processed image 509 in which each traffic laneis identified by the boundary points of a corresponding polygon (e.g.,lane polygon). For example, the physical feature extractor 504 maydetermine, for each traffic lane represented as an edge in thegeographic data 502 b, the boundary points 505 of the correspondingpolygon 510. In the example of FIG. 5B, the processed image 509 includesboundary points 505 of a first polygon 510.

In some aspects, the physical feature extractor 504 may determine thebounding edges of a polygon defining a first traffic lane by at leastdetermining where the first traffic lane intersects with a secondtraffic lane based on a distance between boundary points definingopposite edges of the first traffic lane. For example, the physicalfeature extractor 504 may determine that the first traffic lane isintersecting the second traffic lane when the distance between theboundary points defining opposite edges of the first traffic laneexceeds a threshold defined by the typical width of a traffic lane.Accordingly, a first plurality of boundary points 505 defining the firstpolygon 510 may be determined to include a first boundary point on afirst edge of the first polygon and a second boundary point on a secondedge of the first polygon based on a distance between the first boundarypoint and the second boundary point satisfying a threshold. In someinstances, the typical width of traffic lanes may be determined based ona frequency distribution of a width of traffic lanes. For instance, thephysical feature extractor 504 may apply a histogram indicating, for avariety of width, the frequency of encountering traffic lanes exhibitingeach width. The physical feature extractor 504 may apply the histogramin order to determine the threshold for determining when one trafficlane is intersecting another traffic lane. Moreover, boundary pointsthat fail to satisfy the threshold may be excluded from the polygondefining the traffic lane, thus reducing the quantity of boundary pointsincluded in the polygon.

FIG. 5C depicts a diagram illustrating an example process of filteringlane edges. Lane polygons 510 may be generated as illustrated in theexample of FIG. 5B. In the example of FIG. 5C, a first lane polygon 510a includes a first boundary point 505 a, a second polygon 510 b includesa second boundary point 505 b, and a third lane polygon includes 510 c athird boundary point 505 c. As further shown, the second lane polygon510 b includes a first edge 507 b and the third lane polygon 510 cincludes a second edge 507 c. On the left-hand side of the example inFIG. 5C, the first edge 507 b of the second lane polygon 510 bintersects the second edge 507 c of the third lane polygon 510 c, givingrise to an intersection between the second lane polygon 510 b and thethird lane polygon 510 c in which the third boundary point 505 c of thethird polygon 510 c falls within the second lane polygon 510 b while thesecond boundary point 505 b of the second lane polygon 510 b fallswithin the third lane polygon 510 c. Such lane polygon intersectionartifacts may interfere the accuracy and consistency of subsequentsemantic annotations. As such, in some embodiments, the physical featureextractor 504 may remove lane polygon intersections. For example, asshown on the right hand side of the example in FIG. 5C, lane polygonsidentified as intersecting one another (e.g., the second lane polygon510 b and the third lane polygon 510 c) may be reduced in size toprevent their edges (e.g., the first edge 507 b and the second edge 507c) from intersecting.

FIG. 5D depicts a diagram illustrating an example process of filteringoverlapping lane polygons. In some aspects and depending on a roadnetwork, lane polygons (e.g., first lane polygon 510 a, second lanepolygon 510 b, and/or third lane polygon 510 c) extracted from thesensor data 502 a and/or the geographic data 502 b may overlap.Overlapping lane polygons may cause ambiguities with respect to physicalfeatures, drivable areas, or the like. As such, it may be beneficial toeliminate overlapping regions. In the example of FIG. 5D, a firstpolygon 510 a overlaps with a second polygon 510 b in a region 515. Asshown on the left-hand side of FIG. 5D, the overlapping region 515 maybe removed (e.g., using the perception system 402) from the firstpolygon 510 a and the second polygon 510 b. After removing theoverlapping region 515, a processing system (e.g., the physical featureextractor 504 of perception system 402) may attempt to reconstruct thefirst lane polygon 510 a using the remaining boundary points 505 a aswell as attempt to reconstruct the second lane polygon 510 b using theremaining boundary boundary points 505 b. If a lane polygon cannot bereconstructed, then the perception system 402 may remove the lanepolygon altogether including by removing the remaining portions of thelane polygon. For instance, FIG. 5D shows that the second lane polygon510 b (e.g., the portions of second lane polygon 510 b that remain afterthe removal of the overlapping region 515) is removed as because thesecond lane polygon 510 b cannot be reconstructed with the boundarypoints 505 b that remain subsequent to the removal of the overlappingregion 515.

FIG. 5E depicts a diagram illustrating an example process of filteringand linking lane intersections. As shown on the left-hand side of FIG.5E, once all lanes (e.g., lane polygons 510) have been extracted andfiltered, intersection polygons 520, such as first intersection polygon520 a, second intersection polygon 520 b, and third intersection polygon520 c, may be extracted. In some aspects, intersection extraction may beperformed by removing, from the drivable surface, areas covered bytraffic lanes (e.g., lane polygons 510) and filling in the empty spacesin the drivable area.

As shown on the right-hand side of FIG. 5E, the physical featureextractor 504 may link the contours of the intersection polygons 520with the edges of the lane polygon 510 connected thereto (e.g., thefirst lane polygon 510 a and the second lane polygon 510 b). As shown inthe example of FIG. 5E, the first lane polygon 510 a, the second lanepolygon 510 b, and the third lane polygon 510 c may border the secondintersection polygon 520 b. Accordingly, the physical feature extractor504 may merge edges of the first polygon 510 a, the second polygon 510b, and the third polygon 510 c that border the second intersectionpolygon 520 b to contours of the second intersection polygon 520 b suchthat the lane polygons 510 and the intersection polygon 520 b share atleast one boundary point.

FIG. 5F depicts an example processed image 509 generated by the physicalfeature extractor 504 by performing, based on the sensor data 502 aand/or the geographic data 502 b, one or more of traffic laneextraction, traffic lane edge filtering, overlapping traffic lanefiltering, intersection extraction, and lane section linking. Forexample, the example of the processed image 509 shown in FIG. 5F mayinclude polygons identifying one or more traffic lanes, intersections,and/or the like. In some embodiments, the annotation engine 506 maygenerate, based at least on the processed image 509, one or moresemantic labels for the physical features present therein. For example,the annotation engine 506 may generate semantic labels that identify thephysical features (e.g., traffic lane, intersection, and/or the like)corresponding to the polygons included in the processed image 509.

With reference to FIG. 6 , an illustrative process 600 will be describedfor annotating sensor data (e.g., sensor data 502 a). The process 600may be implemented by the autonomous vehicle computer 400 of FIG. 4Aincluding, for example, the perception system 402.

In the example of FIG. 6 , the process 600 begins at block 602, wherethe autonomous vehicle computer 400 obtains sensor data (e.g., sensordata 502 a). The sensor data may represent an image of a geographicarea. In some aspects, the perception system 402 obtains the sensordata. Sensor data may include data generated by or generally derivablefrom a sensor. For example, the image may be a birds-eye view (e.g.,top-view) of an area around a vehicle 102, a ground-level view from acamera on the vehicle 102, a point cloud generated based on LiDARsensors on the vehicle 102, etc. In one embodiment, the image of thegeographic area includes at least one of a birds-eye view image, aground-level image, or a point cloud image.

At block 604, the autonomous vehicle computer 400 determines, based onthe sensor data, a first plurality of boundary points defining a firstpolygon. The first polygon may be included in the image of thegeographic area. In some aspects, the first polygon may define a trafficlane such as a road, a street, or another pathway. The first pluralityof boundary points defining the first polygon may be determined toinclude a first boundary point on a first edge of the first polygon anda second boundary point on a second edge of the first polygon based on adistance between the first boundary point and the second boundary pointsatisfying a threshold determined based on a frequency distribution ofthe widths of traffic lanes.

At block 606, the autonomous vehicle computer 400 detects an overlapbetween the first polygon and a second polygon. For example, the overlapmay represent an overlapping region of lanes of one or more roads. Theoverlap may include an intersection between a first edge of the firstpolygon and a second edge of the second polygon. The overlap may includean intersection between a first region of the first polygon and a secondregion of the second polygon. In some aspects, each of the first polygonand the second polygon include at least a portion of a traffic lane anda third polygon includes an intersection between two or more trafficlanes.

At block 608, the autonomous vehicle computer 400 modifies at least oneof the first polygon or the second polygon. In some aspects, themodifying may include reducing the at least one of the first polygon orthe second polygon to prevent the first edge from intersecting thesecond edge. For example, at least one of the first polygon or thesecond polygon may be reduced by adjusting at least one of the firstplurality of boundary points including the first polygon or a secondplurality of boundary points including the second polygon. In someaspects, the modifying may include removing the first region from thefirst polygon and removing the second region from the second polygon.

At block 610, the autonomous vehicle computer 400 generates anannotation corresponding to the first polygon. For example, theannotation may identify a physical feature within the geographic areathat corresponds to the first polygon. The physical feature may includea traffic lane (e.g., a drivable surface for motorized vehicles, forbikes, etc.), a cross walk, an intersection, a traffic signal, a trafficsign, and/or the like.

In some aspects, the process 600 may further include determining, usingthe at least one processor, that a polygon cannot be constructed from aremaining plurality of boundary points upon removing the first regionfrom the first polygon. The process 600 may further include in responseto determining that the polygon cannot be reconstructed from theremaining plurality of boundary points upon removing the first regionfrom the first polygon, modifying the first polygon by removing thefirst polygon from the image. The process 600 may further includeidentifying, using the at least one processor and based on the modifiedimage, a third polygon corresponding to an empty space between the firstpolygon and the second polygon. The process 600 may further includegenerating, using the at least one processor, a second annotationcorresponding to the third polygon, the second annotation identifyinganother physical feature within the geographic area. The process 600 mayfurther include merging a first edge of the first polygon and a secondedge of the second polygon with a contour of at least a portion of thethird polygon such that each of the first polygon and the second polygonshare at least one boundary point with the third polygon.

In the foregoing description, aspects and embodiments of the presentdisclosure have been described with reference to numerous specificdetails that can vary from implementation to implementation.Accordingly, the description and drawings are to be regarded in anillustrative rather than a restrictive sense. The sole and exclusiveindicator of the scope of the invention, and what is intended by theapplicants to be the scope of the invention, is the literal andequivalent scope of the set of claims that issue from this application,in the specific form in which such claims issue, including anysubsequent correction. Any definitions expressly set forth herein forterms contained in such claims shall govern the meaning of such terms asused in the claims. In addition, when we use the term “furthercomprising,” in the foregoing description or following claims, whatfollows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A method, comprising: obtaining, using at leastone processor, sensor data representing an image of a geographic area;determining, using the at least one processor and based on the sensordata, a first plurality of boundary points defining a first polygon inthe image of the geographic area; detecting, using the at least oneprocessor and based at least on the first plurality of boundary points,an overlap between the first polygon and a second polygon in the imageof the geographic area; in response to detecting the overlap, modifying,using the at least one processor, at least one of the first polygon orthe second polygon to remove the overlap between the first polygon andthe second polygon; and generating, using the at least one processor andbased on the modifying of the at least one of the first polygon or thesecond polygon, a first annotation corresponding to the first polygon,the first annotation identifying a physical feature within thegeographic area.
 2. The method of claim 1, wherein the overlap comprisesan intersection between a first edge of the first polygon and a secondedge of the second polygon, and wherein the modifying includes reducing,using the at least one processor, the at least one of the first polygonor the second polygon to prevent the first edge from intersecting thesecond edge.
 3. The method of claim 1, wherein the at least one of thefirst polygon or the second polygon are reduced by adjusting at leastone of the first plurality of boundary points comprising the firstpolygon or a second plurality of boundary points comprising the secondpolygon.
 4. The method of claim 1, wherein the overlap comprises anintersection between a first region of the first polygon and a secondregion of the second polygon, and wherein the modifying includesremoving the first region from the first polygon and the second regionfrom the second polygon.
 5. The method of claim 4, further comprising:determining, using the at least one processor, that a polygon cannot beconstructed from a remaining plurality of boundary points upon removingthe first region from the first polygon; and in response to determiningthat the polygon cannot be reconstructed from the remaining plurality ofboundary points upon removing the first region from the first polygon,modifying the first polygon by removing the first polygon from theimage.
 6. The method of claim 1, further comprising: identifying, usingthe at least one processor and based on the modifying, a third polygoncorresponding to an empty space between the first polygon and the secondpolygon; and generating, using the at least one processor, a secondannotation corresponding to the third polygon, the second annotationidentifying another physical feature within the geographic area.
 7. Themethod of claim 1, wherein each of the first polygon and the secondpolygon comprise at least a portion of a traffic lane, and wherein thethird polygon comprises an intersection between two or more trafficlanes.
 8. The method of claim 1, further comprising: merging a firstedge of the first polygon and a second edge of the second polygon with acontour of at least a portion of the third polygon such that each of thefirst polygon and the second polygon share at least one boundary pointwith the third polygon.
 9. The method of claim 1, wherein the firstplurality of boundary points defining the first polygon are determinedto include a first boundary point on a first edge of the first polygonand a second boundary point on a second edge of the first polygon basedon a distance between the first boundary point and the second boundarypoint satisfying a threshold.
 10. The method of claim 1, wherein thedistance between the first boundary point and the second boundary pointare determined to satisfy the threshold based on a frequencydistribution of a width of traffic lanes.
 11. The method of claim 1,wherein the image of the geographic area comprises at least one of abirds-eye view image, a street-level image, or a point cloud image. 12.The method of claim 1, wherein the sensor data comprises electronic mapdata of the geographic area.
 13. A system, comprising: at least oneprocessor, and at least one non-transitory storage media storinginstructions that, when executed by the at least one processor, causethe at least one processor to at least: obtain sensor data representingan image of a geographic area; determine, based on the sensor data, afirst plurality of boundary points defining a first polygon in the imageof the geographic area; detect, based at least on the first plurality ofboundary points, an overlap between the first polygon and a secondpolygon in the image of the geographic area; in response to detectingthe overlap, modify at least one of the first polygon or the secondpolygon to remove the overlap between the first polygon and the secondpolygon; and generate, based on the modifying of the at least one of thefirst polygon or the second polygon, a first annotation corresponding tothe first polygon, the first annotation identifying a physical featurewithin the geographic area.
 14. The system of claim 13, wherein theoverlap comprises an intersection between a first edge of the firstpolygon and a second edge of the second polygon, and wherein themodifying includes reducing, using the at least one processor, the atleast one of the first polygon or the second polygon to prevent thefirst edge from intersecting the second edge.
 15. The system of claim13, wherein the at least one of the first polygon or the second polygonare reduced by adjusting at least one of the first plurality of boundarypoints comprising the first polygon or a second plurality of boundarypoints comprising the second polygon.
 16. The system of claim 13,wherein the overlap comprises an intersection between a first region ofthe first polygon and a second region of the second polygon, and whereinthe modifying includes removing the first region from the first polygonand the second region from the second polygon.
 17. The system of claim13, wherein the processor is further configured to: determine that apolygon cannot be constructed from a remaining plurality of boundarypoints upon removing the first region from the first polygon; and inresponse to determining that the polygon cannot be reconstructed fromthe remaining plurality of boundary points upon removing the firstregion from the first polygon, modify the first polygon by removing thefirst polygon from the image.
 18. The system of claim 13, wherein theprocessor is further configured to: identify, based on the modifiedimage, a third polygon corresponding to an empty space between the firstpolygon and the second polygon; and generate a second annotationcorresponding to the third polygon, the second annotation identifyinganother physical feature within the geographic area
 19. The system ofclaim 13, wherein each of the first polygon and the second polygoncomprise at least a portion of a traffic lane, and wherein the thirdpolygon comprises an intersection between two or more traffic lanes. 20.At least one non-transitory storage media storing instructions that,when executed by a computing system comprising a processor, cause thecomputing system to at least: obtain sensor data representing an imageof a geographic area; determine, based on the sensor data, a firstplurality of boundary points defining a first polygon in the image ofthe geographic area; detect, based at least on the first plurality ofboundary points, an overlap between the first polygon and a secondpolygon in the image of the geographic area; in response to detectingthe overlap, modify at least one of the first polygon or the secondpolygon to remove the overlap between the first polygon and the secondpolygon; and generate, based on the modifying of the at least one of thefirst polygon or the second polygon, a first annotation corresponding tothe first polygon, the first annotation identifying a physical featurewithin the geographic area.